Show of Hands:Votes: Party classification:: Votes_Income
bdanalytics
Date: (Thu) May 26, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Gender.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income" # Done
, "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024"
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(.rnorm) { return(1:length(.rnorm)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(YOB) {
raw <- 2016 - YOB
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(Gender) {
raw <- Gender
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(Income) { raw <- Income;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_Income_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk
#mysavChunk(glbOut$pfx, glbChunks[["last"]])
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Votes_Income_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 6.607 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 1 1938 Male Married (w/kids)
## 2 4 1970 Female over $150,000 Domestic Partners (w/kids)
## 3 5 1997 Male $75,000 - $100,000 Single (no kids)
## 4 8 1983 Male $100,001 - $150,000 Married (w/kids)
## 5 9 1984 Female $50,000 - $74,999 Married (w/kids)
## 6 10 1997 Female over $150,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1 Democrat No No No No
## 2 Bachelor's Degree Democrat Yes No No No
## 3 High School Diploma Republican Yes Yes No
## 4 Bachelor's Degree Democrat No Yes No Yes No
## 5 High School Diploma Republican No Yes No No No
## 6 Current K-12 Democrat No
## Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1 Yes Public No Yes No No No Yes
## 2 Yes Public No Yes No Yes No No Yes
## 3 Yes Private No No No Yes No No Yes
## 4 No Public No Yes No Yes No No Yes
## 5 Yes Public No Yes No Yes Yes No Yes
## 6 Yes Public No No No Yes No Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1 Try first No No Yes Yes
## 2 Science Study first Yes Yes No No Receiving No
## 3 Science Study first Yes No Yes Receiving No
## 4 Science Try first No Yes Yes No Giving Yes
## 5 Art Try first Yes No No No Giving No
## 6 Science Try first Yes Yes No Yes Receiving No
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 1 Yes Idealist No No Yes
## 2 No Pragmatist No No Cool headed Standard hours No
## 3 Yes Pragmatist No Yes Cool headed Odd hours No
## 4 No Idealist No No Cool headed Standard hours No
## 5 No Idealist Yes Yes Hot headed Standard hours No
## 6 No Pragmatist No No Standard hours
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1 Happy Yes Yes No No P.M. Yes Start Yes
## 2 Happy Yes Yes Yes No A.M. No End Yes
## 3 Right Yes No No Yes A.M. Yes Start Yes
## 4 Happy Yes Yes No No A.M. Yes Start Yes
## 5 Happy Yes Yes No Yes P.M. No End No
## 6
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1 No Circumstances Yes Yes Yes Yes No
## 2 No Me Yes Yes No Yes No Mysterious
## 3 Yes Circumstances No Yes No Yes Yes Mysterious
## 4 No Circumstances Yes No No Yes No TMI
## 5 No Me No Yes Yes Yes Yes TMI
## 6
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1 Yes Yes Talk Technology No No Yes
## 2 No No
## 3 No No Tunes Technology Yes Yes Yes Yes
## 4 No No Talk People No Yes Yes Yes
## 5 Yes No Tunes People No No Yes No
## 6
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1 No Demanding No No Cautious No Yes!
## 2 Mac Yes Cautious No Umm...
## 3 No Supportive No PC No Cautious No Umm...
## 4 Yes Supportive No Mac Yes Risk-friendly No Umm...
## 5 No Demanding Yes PC Yes Cautious No Yes!
## 6 Yes Supportive No PC
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1 No Space No In-person Yes No Yes
## 2 No Space Yes In-person No Yes Yes No
## 3 No Space No In-person No No Yes Yes
## 4 No Socialize Yes Online No Yes No Yes
## 5 No Socialize No Online No No Yes Yes
## 6 In-person No No Yes Yes
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people! Yes No Yes Yes No Yes
## 2 Yay people! Yes Yes Yes Yes Yes No Yes
## 3 Grrr people Yes No No No No No No
## 4 Grrr people No No Yes Yes No Yes Yes
## 5 Yay people! Yes No Yes Yes Yes Yes No
## 6 Grrr people Yes No Yes Yes No No Yes
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1 No No No Yes No Own Optimist
## 2
## 3 Yes No No Yes No Own Pessimist Mom
## 4 No No No Yes Yes Own Optimist Mom
## 5 No No Yes No No Own Optimist Mom
## 6 Yes Yes No Yes
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1 Yes Yes No No Nope Yes No No
## 2 No
## 3 No No No No Nope Yes No No No
## 4 No No No Yes Check! No No No Yes
## 5 No Yes Yes Yes Nope Yes No No Yes
## 6
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1 No Only-child No No Yes
## 2 No No Only-child Yes No No
## 3 Yes No Yes No Yes No
## 4 Yes No Yes No No Yes
## 5 No No Yes No No Yes
## 6
## USER_ID YOB Gender Income HouseholdStatus
## 193 245 1964 Male over $150,000 Married (w/kids)
## 848 1046 1953 Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836 3530 1995 Male Single (no kids)
## 4052 5050 1945 Female $75,000 - $100,000 Married (w/kids)
## 4093 5107 1980 Female $100,001 - $150,000 Married (w/kids)
## 5509 6888 1998 Female under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 193 Bachelor's Degree Republican Yes Yes No Yes
## 848 Democrat
## 2836 Current Undergraduate Democrat Yes Yes Yes No
## 4052 Bachelor's Degree Republican
## 4093 Bachelor's Degree Democrat No No
## 5509 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193 No Yes Public No Yes No Yes No
## 848
## 2836 Yes Public Yes No No Yes Yes
## 4052 No Public
## 4093 No No Private No
## 5509 Yes Yes
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 193 No Yes Science Try first Yes Yes Yes No
## 848
## 2836 Yes Yes Art Study first No Yes Yes
## 4052
## 4093 Yes
## 5509 Yes No Art Study first Yes No Yes No
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186
## 193 Giving Yes No Idealist Yes Yes Hot headed
## 848
## 2836 Yes Yes Idealist Yes No Cool headed
## 4052 No No No
## 4093 No No Pragmatist No Yes
## 5509 Giving No
## Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193 Standard hours No Happy Yes Yes No No
## 848
## 2836 Odd hours No Happy Yes Yes No
## 4052
## 4093
## 5509
## Q116197 Q115602 Q115777 Q115610 Q115611 Q115899 Q115390 Q114961
## 193 A.M. Yes End Yes Yes Me No No
## 848
## 2836 Yes End Yes No Circumstances Yes No
## 4052 P.M. Yes Start Yes No No
## 4093 P.M. Yes Start Yes No Circumstances
## 5509
## Q114748 Q115195 Q114517 Q114386 Q113992 Q114152 Q113583 Q113584
## 193 Yes No Yes TMI No Yes Tunes Technology
## 848
## 2836 Yes No No Mysterious No Yes Tunes People
## 4052 No Yes
## 4093 Tunes People
## 5509
## Q113181 Q112478 Q112512 Q112270 Q111848 Q111580 Q111220 Q110740
## 193 No Yes Yes Yes Supportive No Mac
## 848
## 2836 Yes Yes Yes No Yes Demanding Yes PC
## 4052
## 4093 Yes Supportive
## 5509
## Q109367 Q108950 Q109244 Q108855 Q108617 Q108856 Q108754
## 193 No Cautious No Yes! No Socialize No
## 848 Yes Risk-friendly Yes Yes! No Space No
## 2836 Yes Cautious Yes Yes
## 4052
## 4093 No Risk-friendly No Yes! No Space No
## 5509
## Q108342 Q108343 Q107869 Q107491 Q106993 Q106997 Q106272 Q106388
## 193 In-person No Yes Yes No Yay people! Yes Yes
## 848 In-person Yes
## 2836 In-person Yes Yes Yes No
## 4052 No Grrr people
## 4093 In-person Yes Yes Yes Yes Yay people! Yes Yes
## 5509
## Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193 No Yes No No Yes No No No
## 848
## 2836 Yes No No No Yes Yes No No
## 4052 No No No No
## 4093 No No No No Yes No No Yes
## 5509
## Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689 Q100680
## 193 No No Own Optimist Dad Yes Yes No
## 848
## 2836 Yes Yes Rent Optimist Dad No Yes Yes
## 4052 Yes Own No
## 4093 Yes Yes Rent No Yes
## 5509
## Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193 Yes Check! No No No Yes Yes No Yes
## 848
## 2836 Yes Check! No No No Yes Yes Yes
## 4052
## 4093 No Nope Yes No Yes Yes Yes No Yes
## 5509
## Q98078 Q98197 Q96024
## 193 No Yes Yes
## 848 No
## 2836 Yes Yes No
## 4052
## 4093 Yes Yes No
## 5509
## USER_ID YOB Gender Income HouseholdStatus
## 5563 6955 1966 Male over $150,000 Married (w/kids)
## 5564 6956 NA Male
## 5565 6957 2000 Female
## 5566 6958 1969 Male over $150,000
## 5567 6959 1986 Male $25,001 - $50,000 Married (w/kids)
## 5568 6960 1999 Male under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 5563 Bachelor's Degree Democrat
## 5564 Master's Degree Democrat No No
## 5565 Current K-12 Republican
## 5566 Bachelor's Degree Democrat Yes
## 5567 High School Diploma Republican
## 5568 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563 No Yes No Yes Yes
## 5564 No Yes Public Yes
## 5565 Public Yes
## 5566 No No No Yes Yes
## 5567 Yes Yes No
## 5568 Yes No No
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 5563
## 5564
## 5565 Yes Yes Art Try first No Yes Yes Yes
## 5566 Yes Yes Science
## 5567 No No Science No Yes
## 5568
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563
## 5564
## 5565 Receiving
## 5566
## 5567
## 5568
## Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## 'data.frame': 5568 obs. of 20 variables:
## $ USER_ID : int 1 4 5 8 9 10 11 12 13 15 ...
## $ YOB : int 1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Income : chr "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
## $ HouseholdStatus: chr "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
## $ EducationLevel : chr "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
## $ Party : chr "Democrat" "Democrat" "Republican" "Democrat" ...
## $ Q124742 : chr "No" "" "" "No" ...
## $ Q124122 : chr "" "Yes" "Yes" "Yes" ...
## $ Q123464 : chr "No" "No" "Yes" "No" ...
## $ Q123621 : chr "No" "No" "No" "Yes" ...
## $ Q122769 : chr "No" "No" "" "No" ...
## $ Q122770 : chr "Yes" "Yes" "Yes" "No" ...
## $ Q122771 : chr "Public" "Public" "Private" "Public" ...
## $ Q122120 : chr "No" "No" "No" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "No" "No" "No" "No" ...
## $ Q120978 : chr "" "Yes" "Yes" "Yes" ...
## $ Q121011 : chr "No" "No" "No" "No" ...
## $ Q120379 : chr "No" "No" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 20 variables:
## $ Q120650: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q118117: chr "Yes" "No" "Yes" "No" ...
## $ Q118233: chr "No" "No" "No" "No" ...
## $ Q118237: chr "No" "No" "Yes" "No" ...
## $ Q116441: chr "No" "Yes" "No" "No" ...
## $ Q116197: chr "P.M." "A.M." "A.M." "A.M." ...
## $ Q115611: chr "No" "No" "Yes" "No" ...
## $ Q115899: chr "Circumstances" "Me" "Circumstances" "Circumstances" ...
## $ Q115390: chr "Yes" "Yes" "No" "Yes" ...
## $ Q114748: chr "Yes" "No" "No" "No" ...
## $ Q115195: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q113584: chr "Technology" "" "Technology" "People" ...
## $ Q112478: chr "No" "" "Yes" "Yes" ...
## $ Q112270: chr "" "" "Yes" "Yes" ...
## $ Q111848: chr "No" "" "No" "Yes" ...
## $ Q106993: chr "Yes" "No" "Yes" "Yes" ...
## $ Q106388: chr "No" "Yes" "No" "No" ...
## $ Q105655: chr "No" "No" "No" "Yes" ...
## $ Q104996: chr "Yes" "Yes" "No" "Yes" ...
## $ Q102674: chr "No" "" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 21 variables:
## $ Q102674: chr "No" "" "No" "No" ...
## $ Q102687: chr "Yes" "" "Yes" "Yes" ...
## $ Q102289: chr "No" "" "No" "Yes" ...
## $ Q102089: chr "Own" "" "Own" "Own" ...
## $ Q101162: chr "Optimist" "" "Pessimist" "Optimist" ...
## $ Q101163: chr "" "" "Mom" "Mom" ...
## $ Q101596: chr "Yes" "" "No" "No" ...
## $ Q100689: chr "Yes" "" "No" "No" ...
## $ Q100680: chr "No" "" "No" "No" ...
## $ Q100562: chr "No" "" "No" "Yes" ...
## $ Q99982 : chr "Nope" "" "Nope" "Check!" ...
## $ Q100010: chr "Yes" "" "Yes" "No" ...
## $ Q99716 : chr "No" "" "No" "No" ...
## $ Q99581 : chr "No" "" "No" "No" ...
## $ Q99480 : chr "" "No" "No" "Yes" ...
## $ Q98869 : chr "No" "No" "Yes" "Yes" ...
## $ Q98578 : chr "" "No" "No" "No" ...
## $ Q98059 : chr "Only-child" "Only-child" "Yes" "Yes" ...
## $ Q98078 : chr "No" "Yes" "No" "No" ...
## $ Q98197 : chr "No" "No" "Yes" "No" ...
## $ Q96024 : chr "Yes" "No" "No" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 2 1985 Female $25,001 - $50,000 Single (no kids)
## 2 3 1983 Male $50,000 - $74,999 Married (w/kids)
## 3 6 1995 Male $75,000 - $100,000 Single (no kids)
## 4 7 1980 Female $50,000 - $74,999 Single (no kids)
## 5 14 1980 Female Married (no kids)
## 6 28 1973 Male over $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1 Master's Degree Yes No Yes No No
## 2 Current Undergraduate No Yes Yes
## 3 Current K-12
## 4 Master's Degree Yes Yes No Yes Yes Yes
## 5 Current Undergraduate Yes No Yes No No
## 6 Master's Degree No Yes No Yes No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1 Public No Yes Yes Yes No Yes Yes Science
## 2 Public No Yes No
## 3 No No No Yes No Yes Science
## 4 Public No Yes No Yes No Yes Yes Science
## 5 Public Yes Yes No Yes Yes No Yes Art
## 6 Public No Yes No Yes Yes Yes Yes Science
## Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first Yes Yes Yes No Giving Yes No
## 2 Study first No Yes No
## 3 Try first No Yes No Yes Giving
## 4 Try first Yes No No Yes Giving Yes Yes
## 5 Try first Yes Yes Yes Yes Giving No No
## 6 Try first Yes Yes No No Giving No Yes
## Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1 Idealist No Yes Cool headed Odd hours Yes Happy
## 2
## 3
## 4 Idealist No No Cool headed Standard hours No Happy
## 5 Idealist No Yes Hot headed Standard hours Yes Happy
## 6 Pragmatist Yes No Hot headed Odd hours Yes Right
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1 Yes Yes No Yes A.M. Yes End Yes No
## 2 Yes Yes P.M.
## 3 Yes
## 4 Yes No No Yes A.M. Yes Start Yes No
## 5 Yes Yes Yes No P.M. Yes End No No
## 6 Yes Yes Yes Yes P.M. End Yes Yes
## Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1 Me No Yes No Yes Yes TMI
## 2 No Yes
## 3 Yes No Yes Yes No TMI No
## 4 Me Yes No Yes Yes Yes TMI No
## 5 Me No No No Yes No TMI No
## 6 Circumstances No Yes No Yes No TMI Yes
## Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1 No Tunes People Yes Yes No Yes Yes
## 2 No No No Yes
## 3 No Tunes Technology Yes No Yes No
## 4 Yes Talk People No No Yes No Yes
## 5 Tunes Technology No Yes Yes Yes
## 6 No Talk Technology No Yes Yes No Yes
## Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855 Q108617
## 1 Supportive No Yes Cautious Yes Yes!
## 2 No Yes Cautious No Yes! No
## 3 No No No
## 4 Supportive No PC No Cautious Yes Yes! No
## 5 Supportive Yes Mac Yes Cautious No Yes! No
## 6 Demanding No PC Yes Cautious No Umm... No
## Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993 Q106997
## 1 Yes In-person Yes
## 2 Space No Yes Yes Yes Grrr people
## 3 Yes In-person No No Yes Yes Yay people!
## 4 Space No Online No No Yes Yes Yay people!
## 5 Space No In-person No No Yes No Grrr people
## 6 Space No In-person Yes Yes Yes Grrr people
## Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1
## 2 Yes No No Yes No Yes No No
## 3 Yes No Yes No No Yes Yes No No
## 4 No No No No No Yes Yes No No
## 5 No No No Yes Yes Yes Yes Yes No
## 6 Yes No Yes Yes No No No Yes Yes
## Q102674 Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689
## 1 No
## 2 Rent Pessimist Dad
## 3 No No Yes Own Optimist Mom No No
## 4 No No No Own Optimist Dad No No
## 5 Yes No No Own Pessimist Mom No Yes
## 6 Yes Yes No Own Pessimist Mom No Yes
## Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1 Yes Yes Yes
## 2 Yes Yes Yes
## 3 Yes Yes Nope No No No Yes Yes No Yes
## 4 Yes Yes Nope Yes No No No Yes No Yes
## 5 Yes Yes Nope Yes No No Yes No No Yes
## 6 Yes Yes Nope Yes No No Yes No No Yes
## Q98078 Q98197 Q96024
## 1
## 2 Yes No Yes
## 3 No Yes Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## USER_ID YOB Gender Income HouseholdStatus
## 503 2555 1956 Male over $150,000 Married (w/kids)
## 515 2616 1959 Male over $150,000 Married (w/kids)
## 857 4346 1990 Female $50,000 - $74,999
## 950 4814 1969 Male $75,000 - $100,000 Married (w/kids)
## 1207 6057 1937 Female $25,001 - $50,000 Married (no kids)
## 1255 6285 1976 Female $100,001 - $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503 Bachelor's Degree No No No Yes No Yes
## 515 Bachelor's Degree
## 857 Bachelor's Degree
## 950 Bachelor's Degree Yes No Yes No No
## 1207 Bachelor's Degree No Yes
## 1255 Bachelor's Degree
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503 Private No Yes No No Yes No Yes
## 515 No No
## 857 No Yes No No No No Yes
## 950 Public Yes Yes No Yes Yes No Yes
## 1207 Public No Yes No No No No
## 1255
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 503 Science Study first No Yes No Yes Giving Yes
## 515 Yes
## 857 Science Study first No No Yes No Receiving Yes
## 950 Science Study first No No No No Giving No
## 1207 Study first No No Yes Receiving Yes
## 1255
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 503 No Pragmatist No No Cool headed Standard hours No
## 515 No Pragmatist No Yes Cool headed Standard hours No
## 857 Yes Pragmatist No No Cool headed Odd hours No
## 950 No Pragmatist No Yes Hot headed Odd hours Yes
## 1207 No Pragmatist No No Hot headed No
## 1255
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503 Happy Yes Yes No No A.M. Yes End
## 515 Right Yes Yes No Yes Yes
## 857 Right Yes Yes No No A.M. Yes Start
## 950 Happy Yes Yes Yes No P.M. Yes Start
## 1207 Happy Yes Yes No No A.M. Yes Start
## 1255 Yes No Yes A.M. Yes Start
## Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503 Yes Yes Me No No No Yes Yes
## 515 Yes No Me Yes No Yes Yes No
## 857 Yes No Me No No No Yes
## 950 Yes No Me Yes No Yes No No
## 1207 No No Circumstances Yes No Yes No Yes
## 1255 Yes No Circumstances No Yes No Yes Yes
## Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512
## 503 TMI Yes Yes Tunes People Yes No Yes
## 515 No Yes Talk Technology
## 857 Mysterious No No Tunes People No No No
## 950 Mysterious No No Tunes People Yes Yes Yes
## 1207 Yes No Talk Yes
## 1255 TMI Yes Yes Yes
## Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 503 No Yes Demanding No PC No Cautious
## 515 No Yes No Mac Yes
## 857 Yes Yes Supportive No Mac No Risk-friendly
## 950 No Yes Supportive Yes PC No Cautious
## 1207 Supportive No PC Cautious
## 1255 Yes Yes Demanding No Mac
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 503 No Umm... No Space No In-person No Yes
## 515
## 857 Yes Umm... No Space No In-person No Yes
## 950 No Yes! No Space No In-person No No
## 1207 Yes! No Space No In-person No Yes
## 1255
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503 Yes Yes Yay people! Yes No No Yes No
## 515 No
## 857 No Yes Grrr people Yes No Yes No No
## 950 Yes No Grrr people Yes Yes No No No
## 1207 Yes Yes Yes
## 1255
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503 No Yes No No No Yes No Own
## 515 Yes Yes
## 857 No Yes Yes No No Yes Yes Own
## 950 Yes Yes Yes No No Yes No Own
## 1207 Yes
## 1255
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503 Pessimist Mom Yes Yes No Yes Check! Yes
## 515 Check! Yes
## 857 Optimist Mom No Yes Yes No Nope Yes
## 950 Pessimist Mom Yes No No No Check! Yes
## 1207
## 1255
## Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503 No No Yes Yes No Yes Yes Yes Yes
## 515 No Yes Yes Yes No Yes Yes
## 857 No Yes Yes Yes No Yes No No No
## 950 No No Yes Yes No Yes No Yes Yes
## 1207
## 1255
## USER_ID YOB Gender Income HouseholdStatus
## 1387 6922 1988 Male $50,000 - $74,999 Single (no kids)
## 1388 6928 1977 Female $50,000 - $74,999 Domestic Partners (no kids)
## 1389 6930 1998 Female $100,001 - $150,000 Single (no kids)
## 1390 6941 1989 Male $25,001 - $50,000 Married (no kids)
## 1391 6946 1996 Male
## 1392 6947 NA Female
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387 Master's Degree
## 1388 Master's Degree
## 1389 Current K-12 No No
## 1390 Bachelor's Degree
## 1391 Current K-12
## 1392 Yes Yes No No No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387 Yes Yes Yes Yes Yes Yes
## 1388 Yes No Yes
## 1389 Public Yes Yes Yes Yes Yes Yes Yes
## 1390 Yes Yes No No No
## 1391 Yes No No Yes No Yes Yes
## 1392 Public Yes Yes No Yes Yes Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science Try first No Yes Yes No Giving
## 1388 Art
## 1389 Art Study first Yes No Yes No Giving
## 1390
## 1391 Art Study first Yes Yes Yes No Giving
## 1392 Art No No No Yes Giving
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## 'data.frame': 1392 obs. of 20 variables:
## $ USER_ID : int 2 3 6 7 14 28 29 37 44 56 ...
## $ YOB : int 1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ Income : chr "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
## $ HouseholdStatus: chr "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
## $ EducationLevel : chr "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
## $ Q124742 : chr "" "" "" "Yes" ...
## $ Q124122 : chr "Yes" "" "" "Yes" ...
## $ Q123464 : chr "No" "No" "" "No" ...
## $ Q123621 : chr "Yes" "" "" "Yes" ...
## $ Q122769 : chr "No" "Yes" "" "Yes" ...
## $ Q122770 : chr "No" "Yes" "" "Yes" ...
## $ Q122771 : chr "Public" "Public" "" "Public" ...
## $ Q122120 : chr "No" "No" "" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "Yes" "No" "No" "No" ...
## $ Q120978 : chr "Yes" "" "No" "Yes" ...
## $ Q121011 : chr "No" "" "Yes" "No" ...
## $ Q120379 : chr "Yes" "" "No" "Yes" ...
## $ Q120650 : chr "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 20 variables:
## $ Q120012: chr "Yes" "No" "No" "Yes" ...
## $ Q120014: chr "Yes" "Yes" "Yes" "No" ...
## $ Q118117: chr "No" "" "" "Yes" ...
## $ Q118237: chr "Yes" "" "" "No" ...
## $ Q116953: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q116601: chr "Yes" "Yes" "" "No" ...
## $ Q116448: chr "Yes" "" "" "Yes" ...
## $ Q116197: chr "A.M." "P.M." "" "A.M." ...
## $ Q115899: chr "Me" "" "" "Me" ...
## $ Q114961: chr "Yes" "" "No" "No" ...
## $ Q113584: chr "People" "" "Technology" "People" ...
## $ Q113181: chr "Yes" "No" "Yes" "No" ...
## $ Q112512: chr "No" "" "Yes" "Yes" ...
## $ Q108950: chr "Cautious" "Cautious" "" "Cautious" ...
## $ Q108617: chr "" "No" "No" "No" ...
## $ Q108342: chr "In-person" "" "In-person" "Online" ...
## $ Q107491: chr "" "Yes" "Yes" "Yes" ...
## $ Q106272: chr "" "Yes" "Yes" "No" ...
## $ Q106389: chr "" "No" "Yes" "No" ...
## $ Q104996: chr "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 21 variables:
## $ Q102674: chr "" "" "No" "No" ...
## $ Q102687: chr "" "" "No" "No" ...
## $ Q102289: chr "" "" "Yes" "No" ...
## $ Q102089: chr "" "Rent" "Own" "Own" ...
## $ Q101162: chr "" "Pessimist" "Optimist" "Optimist" ...
## $ Q101163: chr "" "Dad" "Mom" "Dad" ...
## $ Q101596: chr "" "" "No" "No" ...
## $ Q100689: chr "No" "" "No" "No" ...
## $ Q100680: chr "Yes" "" "Yes" "Yes" ...
## $ Q100562: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q99982 : chr "" "" "Nope" "Nope" ...
## $ Q100010: chr "" "" "No" "Yes" ...
## $ Q99716 : chr "" "" "No" "No" ...
## $ Q99581 : chr "" "" "No" "No" ...
## $ Q99480 : chr "" "" "Yes" "No" ...
## $ Q98869 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q98578 : chr "" "" "No" "No" ...
## $ Q98059 : chr "" "Yes" "Yes" "Yes" ...
## $ Q98078 : chr "" "Yes" "No" "No" ...
## $ Q98197 : chr "" "No" "Yes" "No" ...
## $ Q96024 : chr "" "Yes" "Yes" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392
## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392## Loading required package: RColorBrewer
## .src .n
## 1 Train 5568
## 2 Test 1392## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 6.607 13.426 6.819
## 2 inspect.data 2 0 0 13.427 NA NAStep 2.0: inspect data
## Warning: Removed 1392 rows containing non-finite values (stat_count).## Loading required package: reshape2
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1392
## Train 2951 2617 NA
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1
## Train 0.5299928 0.4700072 NA
## [1] "numeric data missing in glbObsAll: "
## YOB
## 415
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## Party Party.fctr .n
## 1 Democrat D 2951
## 2 Republican R 2617
## 3 <NA> <NA> 1392## Warning: Removed 1 rows containing missing values (position_stack).
## Party.fctr.R Party.fctr.D Party.fctr.NA
## Test NA NA 1392
## Train 2617 2951 NA
## Party.fctr.R Party.fctr.D Party.fctr.NA
## Test NA NA 1
## Train 0.4700072 0.5299928 NA
## [1] "elapsed Time (secs): 8.407000"## Loading required package: caTools## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "elapsed Time (secs): 7.590000"
## [1] "elapsed Time (secs): 7.590000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 13.427 31.341 17.914
## 3 scrub.data 2 1 1 31.341 NA NAStep 2.1: scrub data
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 31.341 35.067 3.726
## 4 transform.data 2 2 2 35.067 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 35.067 35.107 0.04
## 5 extract.features 3 0 0 35.108 NA NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 35.108
## 6 extract.features.datetime 3 1 1 35.128
## end elapsed
## 5 35.127 0.02
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 35.154
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 35.128
## 7 extract.features.image 3 2 2 35.165
## end elapsed
## 6 35.164 0.036
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 35.204 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 35.204
## 2 extract.features.image.end 2 0 0 35.214
## end elapsed
## 1 35.213 0.01
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 35.204
## 2 extract.features.image.end 2 0 0 35.214
## end elapsed
## 1 35.213 0.01
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 35.165 35.225
## 8 extract.features.price 3 3 3 35.225 NA
## elapsed
## 7 0.06
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 35.251 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 35.225 35.26
## 9 extract.features.text 3 4 4 35.261 NA
## elapsed
## 8 0.036
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 35.302 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 35.261 35.311
## 10 extract.features.string 3 5 5 35.312 NA
## elapsed
## 9 0.05
## 10 NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn end
## 1 extract.features.string.bgn 1 0 0 35.344 NA
## elapsed
## 1 NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 35.344 35.354 0.01
## 2 0 35.354 NA NA## Gender Income HouseholdStatus EducationLevel
## "Gender" "Income" "HouseholdStatus" "EducationLevel"
## Party Q124742 Q124122 Q123464
## "Party" "Q124742" "Q124122" "Q123464"
## Q123621 Q122769 Q122770 Q122771
## "Q123621" "Q122769" "Q122770" "Q122771"
## Q122120 Q121699 Q121700 Q120978
## "Q122120" "Q121699" "Q121700" "Q120978"
## Q121011 Q120379 Q120650 Q120472
## "Q121011" "Q120379" "Q120650" "Q120472"
## Q120194 Q120012 Q120014 Q119334
## "Q120194" "Q120012" "Q120014" "Q119334"
## Q119851 Q119650 Q118892 Q118117
## "Q119851" "Q119650" "Q118892" "Q118117"
## Q118232 Q118233 Q118237 Q117186
## "Q118232" "Q118233" "Q118237" "Q117186"
## Q117193 Q116797 Q116881 Q116953
## "Q117193" "Q116797" "Q116881" "Q116953"
## Q116601 Q116441 Q116448 Q116197
## "Q116601" "Q116441" "Q116448" "Q116197"
## Q115602 Q115777 Q115610 Q115611
## "Q115602" "Q115777" "Q115610" "Q115611"
## Q115899 Q115390 Q114961 Q114748
## "Q115899" "Q115390" "Q114961" "Q114748"
## Q115195 Q114517 Q114386 Q113992
## "Q115195" "Q114517" "Q114386" "Q113992"
## Q114152 Q113583 Q113584 Q113181
## "Q114152" "Q113583" "Q113584" "Q113181"
## Q112478 Q112512 Q112270 Q111848
## "Q112478" "Q112512" "Q112270" "Q111848"
## Q111580 Q111220 Q110740 Q109367
## "Q111580" "Q111220" "Q110740" "Q109367"
## Q108950 Q109244 Q108855 Q108617
## "Q108950" "Q109244" "Q108855" "Q108617"
## Q108856 Q108754 Q108342 Q108343
## "Q108856" "Q108754" "Q108342" "Q108343"
## Q107869 Q107491 Q106993 Q106997
## "Q107869" "Q107491" "Q106993" "Q106997"
## Q106272 Q106388 Q106389 Q106042
## "Q106272" "Q106388" "Q106389" "Q106042"
## Q105840 Q105655 Q104996 Q103293
## "Q105840" "Q105655" "Q104996" "Q103293"
## Q102906 Q102674 Q102687 Q102289
## "Q102906" "Q102674" "Q102687" "Q102289"
## Q102089 Q101162 Q101163 Q101596
## "Q102089" "Q101162" "Q101163" "Q101596"
## Q100689 Q100680 Q100562 Q99982
## "Q100689" "Q100680" "Q100562" "Q99982"
## Q100010 Q99716 Q99581 Q99480
## "Q100010" "Q99716" "Q99581" "Q99480"
## Q98869 Q98578 Q98059 Q98078
## "Q98869" "Q98578" "Q98059" "Q98078"
## Q98197 Q96024 .src
## "Q98197" "Q96024" ".src"## label step_major step_minor label_minor bgn end
## 10 extract.features.string 3 5 5 35.312 35.372
## 11 extract.features.end 3 6 6 35.372 NA
## elapsed
## 10 0.06
## 11 NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 35.372 36.257
## 12 manage.missing.data 4 0 0 36.257 NA
## elapsed
## 11 0.885
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 36.257 36.66
## 13 cluster.data 5 0 0 36.661 NA
## elapsed
## 12 0.403
## 13 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn end
## 13 cluster.data 5 0 0 36.661 36.735
## 14 partition.data.training 6 0 0 36.735 NA
## elapsed
## 13 0.074
## 14 NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.13 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.13 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## [1] "lclgetMatrixCorrelation: duration: 40.870000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 14.422000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 51.304000 secs"
## [1] "cor of Fit vs. New: 1.0000"## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 107.22 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 1392
## Fit 2360 2092 NA
## OOB 591 525 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5300988 0.4699012 NA
## OOB 0.5295699 0.4704301 NA
## Gender.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 2 M 2655 670 837 0.5963612 0.60035842
## 1 F 1709 421 525 0.3838724 0.37724014
## 3 N 88 25 30 0.0197664 0.02240143
## .freqRatio.Tst
## 2 0.60129310
## 1 0.37715517
## 3 0.02155172## [1] "glbObsAll: "## [1] 6960 116## [1] "glbObsTrn: "## [1] 5568 116## [1] "glbObsFit: "## [1] 4452 115## [1] "glbObsOOB: "## [1] 1116 115## [1] "glbObsNew: "## [1] 1392 115## [1] "partition.data.training chunk: teardown: elapsed: 107.80 secs"## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 36.735
## 15 select.features 7 0 0 144.592
## end elapsed
## 14 144.592 107.857
## 15 NA NAStep 7.0: select features
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## YOB.Age.fctr 0.012919850 0 0.012919850 NA 1.005794
## .rnorm -0.007803952 0 0.007803952 NA 1.000000
## YOB -0.011682820 1 0.011682820 NA 1.027559
## Income.fctr -0.015963546 0 0.015963546 NA 1.256724
## .pos -0.030203714 1 0.030203714 NA 1.000000
## USER_ID -0.030230487 1 0.030230487 NA 1.000000
## Gender.fctr -0.102740085 0 0.102740085 NA 1.561033
## percentUnique zeroVar nzv is.cor.y.abs.low
## YOB.Age.fctr 0.16163793 FALSE FALSE FALSE
## .rnorm 100.00000000 FALSE FALSE FALSE
## YOB 1.41882184 FALSE FALSE FALSE
## Income.fctr 0.12571839 FALSE FALSE FALSE
## .pos 100.00000000 FALSE FALSE FALSE
## USER_ID 100.00000000 FALSE FALSE FALSE
## Gender.fctr 0.05387931 FALSE FALSE FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392## [1] "glb_feats_df:"## [1] 7 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID -0.03023049 TRUE 0.03023049 NA
## Party.fctr Party.fctr NA TRUE NA NA
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID NA NA FALSE TRUE
## Party.fctr NA NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 15 select.features 7 0 0 144.592 146.605
## 16 fit.models 8 0 0 146.606 NA
## elapsed
## 15 2.014
## 16 NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 147.077 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 147.077 147.109
## 2 fit.models_0_MFO 1 1 myMFO_classfr 147.110 NA
## elapsed
## 1 0.032
## 2 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.441000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
## D R
## 0.5300988 0.4699012
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.825000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.827000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5300988 0.4699012
## 2 0.5300988 0.4699012
## 3 0.5300988 0.4699012
## 4 0.5300988 0.4699012
## 5 0.5300988 0.4699012
## 6 0.5300988 0.4699012
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5300988 0.4699012
## 2 0.5300988 0.4699012
## 3 0.5300988 0.4699012
## 4 0.5300988 0.4699012
## 5 0.5300988 0.4699012
## 6 0.5300988 0.4699012
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 3.228000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.374
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 0 1
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0.6393643 0.4699012
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4551521 0.4846898 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 0 1 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6398537 0.4704301
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4408094 0.5002071 0
## [1] "myfit_mdl: exit: 3.237000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 147.110
## 3 fit.models_0_Random 1 2 myrandom_classfr 150.353
## end elapsed
## 2 150.352 3.242
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.401000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.686000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.687000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 4.524000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.278 0.002 0.5012291
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.4694073 0.5330508 0.5039861 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6393643 0.4699012 0.4551521
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4846898 0 0.5263863 0.5028571
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5499154 0.5173024 0.6 0.6398537
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4704301 0.4408094 0.5002071
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 4.536000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 150.353 154.905 4.552
## 4 154.906 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Gender.fctr,Income.fctr"
## [1] "myfit_mdl: setup complete: 0.670000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00137 on full training set
## [1] "myfit_mdl: train complete: 1.466000 secs"
## Length Class Mode
## a0 47 -none- numeric
## beta 376 dgCMatrix S4
## df 47 -none- numeric
## dim 2 -none- numeric
## lambda 47 -none- numeric
## dev.ratio 47 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 8 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.L Income.fctr.Q
## 0.236089011 0.222774778 -0.330625516 -0.025769103 -0.184632333
## Income.fctr.C Income.fctr^4 Income.fctr^5 Income.fctr^6
## -0.145699886 -0.043998920 -0.036393892 0.009362218
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Income.fctr.L"
## [5] "Income.fctr.Q" "Income.fctr.C" "Income.fctr^4" "Income.fctr^5"
## [9] "Income.fctr^6"
## [1] "myfit_mdl: train diagnostics complete: 1.568000 secs"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 3.816000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Income.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.785 0.054 0.5657719
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5874761 0.5440678 0.4188611 0.7
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6393643 0.4699012 0.4551521
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4846898 0 0.498685 0.5219048
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4754653 0.5075175 0.7 0.6398537
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4704301 0.4408094 0.5002071
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 3.828000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Gender.fctr,Income.fctr"
## [1] "myfit_mdl: setup complete: 0.686000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0332 on full training set
## [1] "myfit_mdl: train complete: 2.599000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4452
##
## CP nsplit rel error
## 1 0.06644359 0 1.0000000
## 2 0.03322180 1 0.9335564
##
## Variable importance
## Gender.fctrM Gender.fctrF
## 51 49
##
## Node number 1: 4452 observations, complexity param=0.06644359
## predicted class=D expected loss=0.4699012 P(node) =1
## class counts: 2092 2360
## probabilities: 0.470 0.530
## left son=2 (2655 obs) right son=3 (1797 obs)
## Primary splits:
## Gender.fctrM < 0.5 to the right, improve=41.662550, (0 missing)
## Gender.fctrF < 0.5 to the left, improve=40.521580, (0 missing)
## Income.fctr.L < 0.4724556 to the right, improve= 5.566044, (0 missing)
## Income.fctr.Q < 0.5455447 to the right, improve= 5.566044, (0 missing)
## Income.fctr.C < 0.2041241 to the right, improve= 2.396973, (0 missing)
## Surrogate splits:
## Gender.fctrF < 0.5 to the left, agree=0.98, adj=0.951, (0 split)
##
## Node number 2: 2655 observations
## predicted class=R expected loss=0.473823 P(node) =0.5963612
## class counts: 1397 1258
## probabilities: 0.526 0.474
##
## Node number 3: 1797 observations
## predicted class=D expected loss=0.3867557 P(node) =0.4036388
## class counts: 695 1102
## probabilities: 0.387 0.613
##
## n= 4452
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4452 2092 D (0.4699012 0.5300988)
## 2) Gender.fctrM>=0.5 2655 1258 R (0.5261770 0.4738230) *
## 3) Gender.fctrM< 0.5 1797 695 D (0.3867557 0.6132443) *
## [1] "myfit_mdl: train diagnostics complete: 3.620000 secs"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 5.998000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Gender.fctr,Income.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.904 0.026 0.5673656
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.667782 0.4669492 0.4326344 0.7
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6393643 0.5606472 0.4551521
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4846898 0.1315916 0.4996616 0.6
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.3993232 0.5003384 0.7 0.6398537
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4704301 0.4408094 0.5002071
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.008033031 0.01681127
## [1] "myfit_mdl: exit: 6.014000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet 154.906
## 5 fit.models_0_Low.cor.X 1 4 glmnet 164.801
## end elapsed
## 4 164.8 9.894
## 5 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.679000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00635 on full training set
## [1] "myfit_mdl: train complete: 3.505000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 47 -none- numeric
## beta 799 dgCMatrix S4
## df 47 -none- numeric
## dim 2 -none- numeric
## lambda 47 -none- numeric
## dev.ratio 47 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 17 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.274560219 0.161777041 -0.353285032 -0.124989413 -0.093190698
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.009643051 0.078769862 -0.091108524 -0.160333965
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.27637347 0.16324101 -0.35692452 -0.13118580 -0.10036052
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.01732754 0.08676439 -0.09901607 -0.16937001
## [1] "myfit_mdl: train diagnostics complete: 4.075000 secs"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 6.501000 secs"
## id feats
## 1 Low.cor.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 2.814 0.067
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.572091 0.5607075 0.5834746 0.4107806
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6393643 0.5634945
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4551521 0.4846898 0.1234163
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4993087 0.487619 0.5109983 0.5065619
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6398537 0.4704301
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4408094 0.5002071 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008526242 0.01867611
## [1] "myfit_mdl: exit: 6.514000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_0_Low.cor.X 1 4 glmnet 164.801 171.338
## 6 fit.models_0_end 1 5 teardown 171.338 NA
## elapsed
## 5 6.537
## 6 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 146.606 171.35 24.744
## 17 fit.models 8 1 1 171.350 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 174.606 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 174.606 174.616
## 2 fit.models_1_All.X 1 1 setup 174.617 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 174.617 174.624
## 3 fit.models_1_All.X 1 2 glmnet 174.625 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.677000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00635 on full training set
## [1] "myfit_mdl: train complete: 3.338000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 47 -none- numeric
## beta 799 dgCMatrix S4
## df 47 -none- numeric
## dim 2 -none- numeric
## lambda 47 -none- numeric
## dev.ratio 47 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 17 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.274560219 0.161777041 -0.353285032 -0.124989413 -0.093190698
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.009643051 0.078769862 -0.091108524 -0.160333965
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.27637347 0.16324101 -0.35692452 -0.13118580 -0.10036052
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.01732754 0.08676439 -0.09901607 -0.16937001
## [1] "myfit_mdl: train diagnostics complete: 4.004000 secs"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 6.811000 secs"
## id feats
## 1 All.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 2.649 0.07
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.572091 0.5607075 0.5834746 0.4107806
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6393643 0.5634945
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4551521 0.4846898 0.1234163
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4993087 0.487619 0.5109983 0.5065619
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6398537 0.4704301
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4408094 0.5002071 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008526242 0.01867611
## [1] "myfit_mdl: exit: 6.825000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 174.625 181.455
## 4 fit.models_1_All.X 1 3 glm 181.456 NA
## elapsed
## 3 6.831
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.673000 secs"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.159000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5315 -1.1611 0.9176 1.1319 1.4309
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.313705 0.218375 1.437 0.15085
## .rnorm 0.001148 0.030006 0.038 0.96948
## Gender.fctrF 0.162424 0.224209 0.724 0.46880
## Gender.fctrM -0.408408 0.222339 -1.837 0.06623 .
## Income.fctr.L -0.050918 0.087798 -0.580 0.56195
## Income.fctr.Q -0.204312 0.084110 -2.429 0.01514 *
## Income.fctr.C -0.165851 0.083914 -1.976 0.04811 *
## `Income.fctr^4` -0.045150 0.081964 -0.551 0.58173
## `Income.fctr^5` -0.060185 0.084169 -0.715 0.47458
## `Income.fctr^6` 0.022628 0.082671 0.274 0.78430
## YOB.Age.fctr.L 0.149602 0.118095 1.267 0.20523
## YOB.Age.fctr.Q 0.054182 0.119165 0.455 0.64934
## YOB.Age.fctr.C 0.054604 0.106443 0.513 0.60796
## `YOB.Age.fctr^4` 0.006938 0.098258 0.071 0.94371
## `YOB.Age.fctr^5` 0.046417 0.092397 0.502 0.61541
## `YOB.Age.fctr^6` 0.174259 0.084810 2.055 0.03991 *
## `YOB.Age.fctr^7` -0.177139 0.086090 -2.058 0.03963 *
## `YOB.Age.fctr^8` -0.261042 0.092887 -2.810 0.00495 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6155.6 on 4451 degrees of freedom
## Residual deviance: 6043.5 on 4434 degrees of freedom
## AIC: 6079.5
##
## Number of Fisher Scoring iterations: 4
##
## [1] "myfit_mdl: train diagnostics complete: 3.392000 secs"
## Prediction
## Reference R D
## R 2092 0
## D 2360 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4699012 0.0000000 0.4551521 0.4846898 0.5300988
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## Prediction
## Reference R D
## R 525 0
## D 591 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.704301e-01 0.000000e+00 4.408094e-01 5.002071e-01 5.295699e-01
## AccuracyPValue McnemarPValue
## 9.999661e-01 4.131000e-130
## [1] "myfit_mdl: predict complete: 8.631000 secs"
## id feats
## 1 All.X##rcv#glm YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.475 0.048
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.566508 0.5258126 0.6072034 0.4106305
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6393643 0.5594529
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4551521 0.4846898 0.1113987
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4980276 0.4495238 0.5465313 0.505023
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6398537 0.4704301
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4408094 0.5002071 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01177408 0.02583235
## [1] "myfit_mdl: exit: 8.644000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 181.456 190.157
## 5 fit.models_1_preProc 1 4 preProc 190.157 NA
## elapsed
## 4 8.701
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Income.fctr
## Max.cor.Y##rcv#rpart Gender.fctr,Income.fctr
## Low.cor.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## All.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## All.X##rcv#glm YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.374
## Random###myrandom_classfr 0 0.278
## Max.cor.Y.rcv.1X1###glmnet 0 0.785
## Max.cor.Y##rcv#rpart 5 1.904
## Low.cor.X##rcv#glmnet 20 2.814
## All.X##rcv#glmnet 20 2.649
## All.X##rcv#glm 1 1.475
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.003 0.5000000
## Random###myrandom_classfr 0.002 0.5012291
## Max.cor.Y.rcv.1X1###glmnet 0.054 0.5657719
## Max.cor.Y##rcv#rpart 0.026 0.5673656
## Low.cor.X##rcv#glmnet 0.067 0.5720910
## All.X##rcv#glmnet 0.070 0.5720910
## All.X##rcv#glm 0.048 0.5665080
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4694073 0.5330508 0.5039861
## Max.cor.Y.rcv.1X1###glmnet 0.5874761 0.5440678 0.4188611
## Max.cor.Y##rcv#rpart 0.6677820 0.4669492 0.4326344
## Low.cor.X##rcv#glmnet 0.5607075 0.5834746 0.4107806
## All.X##rcv#glmnet 0.5607075 0.5834746 0.4107806
## All.X##rcv#glm 0.5258126 0.6072034 0.4106305
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.5 0.6393643
## Random###myrandom_classfr 0.6 0.6393643
## Max.cor.Y.rcv.1X1###glmnet 0.7 0.6393643
## Max.cor.Y##rcv#rpart 0.7 0.6393643
## Low.cor.X##rcv#glmnet 0.7 0.6393643
## All.X##rcv#glmnet 0.7 0.6393643
## All.X##rcv#glm 0.7 0.6393643
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.4699012 0.4551521
## Random###myrandom_classfr 0.4699012 0.4551521
## Max.cor.Y.rcv.1X1###glmnet 0.4699012 0.4551521
## Max.cor.Y##rcv#rpart 0.5606472 0.4551521
## Low.cor.X##rcv#glmnet 0.5634945 0.4551521
## All.X##rcv#glmnet 0.5634945 0.4551521
## All.X##rcv#glm 0.5594529 0.4551521
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4846898 0.0000000
## Random###myrandom_classfr 0.4846898 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.4846898 0.0000000
## Max.cor.Y##rcv#rpart 0.4846898 0.1315916
## Low.cor.X##rcv#glmnet 0.4846898 0.1234163
## All.X##rcv#glmnet 0.4846898 0.1234163
## All.X##rcv#glm 0.4846898 0.1113987
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5263863 0.5028571 0.5499154
## Max.cor.Y.rcv.1X1###glmnet 0.4986850 0.5219048 0.4754653
## Max.cor.Y##rcv#rpart 0.4996616 0.6000000 0.3993232
## Low.cor.X##rcv#glmnet 0.4993087 0.4876190 0.5109983
## All.X##rcv#glmnet 0.4993087 0.4876190 0.5109983
## All.X##rcv#glm 0.4980276 0.4495238 0.5465313
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.5
## Random###myrandom_classfr 0.5173024 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.5075175 0.7
## Max.cor.Y##rcv#rpart 0.5003384 0.7
## Low.cor.X##rcv#glmnet 0.5065619 0.7
## All.X##rcv#glmnet 0.5065619 0.7
## All.X##rcv#glm 0.5050230 0.7
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6398537 0.4704301
## Random###myrandom_classfr 0.6398537 0.4704301
## Max.cor.Y.rcv.1X1###glmnet 0.6398537 0.4704301
## Max.cor.Y##rcv#rpart 0.6398537 0.4704301
## Low.cor.X##rcv#glmnet 0.6398537 0.4704301
## All.X##rcv#glmnet 0.6398537 0.4704301
## All.X##rcv#glm 0.6398537 0.4704301
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO###myMFO_classfr 0.4408094 0.5002071
## Random###myrandom_classfr 0.4408094 0.5002071
## Max.cor.Y.rcv.1X1###glmnet 0.4408094 0.5002071
## Max.cor.Y##rcv#rpart 0.4408094 0.5002071
## Low.cor.X##rcv#glmnet 0.4408094 0.5002071
## All.X##rcv#glmnet 0.4408094 0.5002071
## All.X##rcv#glm 0.4408094 0.5002071
## max.Kappa.OOB max.AccuracySD.fit
## MFO###myMFO_classfr 0 NA
## Random###myrandom_classfr 0 NA
## Max.cor.Y.rcv.1X1###glmnet 0 NA
## Max.cor.Y##rcv#rpart 0 0.008033031
## Low.cor.X##rcv#glmnet 0 0.008526242
## All.X##rcv#glmnet 0 0.008526242
## All.X##rcv#glm 0 0.011774076
## max.KappaSD.fit
## MFO###myMFO_classfr NA
## Random###myrandom_classfr NA
## Max.cor.Y.rcv.1X1###glmnet NA
## Max.cor.Y##rcv#rpart 0.01681127
## Low.cor.X##rcv#glmnet 0.01867611
## All.X##rcv#glmnet 0.01867611
## All.X##rcv#glm 0.02583235rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 190.157 190.232
## 6 fit.models_1_end 1 5 teardown 190.233 NA
## elapsed
## 5 0.075
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 171.350 190.241 18.891
## 18 fit.models 8 2 2 190.241 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 193.965 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Income.fctr
## Max.cor.Y##rcv#rpart Gender.fctr,Income.fctr
## Low.cor.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## All.X##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## All.X##rcv#glm YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns max.AUCpROC.fit max.Sens.fit
## MFO###myMFO_classfr 0 0.5000000 0.0000000
## Random###myrandom_classfr 0 0.5012291 0.4694073
## Max.cor.Y.rcv.1X1###glmnet 0 0.5657719 0.5874761
## Max.cor.Y##rcv#rpart 5 0.5673656 0.6677820
## Low.cor.X##rcv#glmnet 20 0.5720910 0.5607075
## All.X##rcv#glmnet 20 0.5720910 0.5607075
## All.X##rcv#glm 1 0.5665080 0.5258126
## max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.5000000
## Random###myrandom_classfr 0.5330508 0.5039861
## Max.cor.Y.rcv.1X1###glmnet 0.5440678 0.4188611
## Max.cor.Y##rcv#rpart 0.4669492 0.4326344
## Low.cor.X##rcv#glmnet 0.5834746 0.4107806
## All.X##rcv#glmnet 0.5834746 0.4107806
## All.X##rcv#glm 0.6072034 0.4106305
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.5 0.6393643
## Random###myrandom_classfr 0.6 0.6393643
## Max.cor.Y.rcv.1X1###glmnet 0.7 0.6393643
## Max.cor.Y##rcv#rpart 0.7 0.6393643
## Low.cor.X##rcv#glmnet 0.7 0.6393643
## All.X##rcv#glmnet 0.7 0.6393643
## All.X##rcv#glm 0.7 0.6393643
## max.Accuracy.fit max.Kappa.fit max.AUCpROC.OOB
## MFO###myMFO_classfr 0.4699012 0.0000000 0.5000000
## Random###myrandom_classfr 0.4699012 0.0000000 0.5263863
## Max.cor.Y.rcv.1X1###glmnet 0.4699012 0.0000000 0.4986850
## Max.cor.Y##rcv#rpart 0.5606472 0.1315916 0.4996616
## Low.cor.X##rcv#glmnet 0.5634945 0.1234163 0.4993087
## All.X##rcv#glmnet 0.5634945 0.1234163 0.4993087
## All.X##rcv#glm 0.5594529 0.1113987 0.4980276
## max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.5028571 0.5499154 0.5173024
## Max.cor.Y.rcv.1X1###glmnet 0.5219048 0.4754653 0.5075175
## Max.cor.Y##rcv#rpart 0.6000000 0.3993232 0.5003384
## Low.cor.X##rcv#glmnet 0.4876190 0.5109983 0.5065619
## All.X##rcv#glmnet 0.4876190 0.5109983 0.5065619
## All.X##rcv#glm 0.4495238 0.5465313 0.5050230
## opt.prob.threshold.OOB max.f.score.OOB
## MFO###myMFO_classfr 0.5 0.6398537
## Random###myrandom_classfr 0.6 0.6398537
## Max.cor.Y.rcv.1X1###glmnet 0.7 0.6398537
## Max.cor.Y##rcv#rpart 0.7 0.6398537
## Low.cor.X##rcv#glmnet 0.7 0.6398537
## All.X##rcv#glmnet 0.7 0.6398537
## All.X##rcv#glm 0.7 0.6398537
## max.Accuracy.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.4704301 0
## Random###myrandom_classfr 0.4704301 0
## Max.cor.Y.rcv.1X1###glmnet 0.4704301 0
## Max.cor.Y##rcv#rpart 0.4704301 0
## Low.cor.X##rcv#glmnet 0.4704301 0
## All.X##rcv#glmnet 0.4704301 0
## All.X##rcv#glm 0.4704301 0
## inv.elapsedtime.everything
## MFO###myMFO_classfr 2.6737968
## Random###myrandom_classfr 3.5971223
## Max.cor.Y.rcv.1X1###glmnet 1.2738854
## Max.cor.Y##rcv#rpart 0.5252101
## Low.cor.X##rcv#glmnet 0.3553660
## All.X##rcv#glmnet 0.3775009
## All.X##rcv#glm 0.6779661
## inv.elapsedtime.final
## MFO###myMFO_classfr 333.33333
## Random###myrandom_classfr 500.00000
## Max.cor.Y.rcv.1X1###glmnet 18.51852
## Max.cor.Y##rcv#rpart 38.46154
## Low.cor.X##rcv#glmnet 14.92537
## All.X##rcv#glmnet 14.28571
## All.X##rcv#glm 20.83333# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 2 Random###myrandom_classfr 0.4704301 0.5173024
## 3 Max.cor.Y.rcv.1X1###glmnet 0.4704301 0.5075175
## 5 Low.cor.X##rcv#glmnet 0.4704301 0.5065619
## 6 All.X##rcv#glmnet 0.4704301 0.5065619
## 7 All.X##rcv#glm 0.4704301 0.5050230
## 4 Max.cor.Y##rcv#rpart 0.4704301 0.5003384
## 1 MFO###myMFO_classfr 0.4704301 0.5000000
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 2 0.5263863 0.4699012 0.6
## 3 0.4986850 0.4699012 0.7
## 5 0.4993087 0.5634945 0.7
## 6 0.4993087 0.5634945 0.7
## 7 0.4980276 0.5594529 0.7
## 4 0.4996616 0.5606472 0.7
## 1 0.5000000 0.4699012 0.5
## opt.prob.threshold.OOB
## 2 0.6
## 3 0.7
## 5 0.7
## 6 0.7
## 7 0.7
## 4 0.7
## 1 0.5# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7ff74084f118>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Random###myrandom_classfr"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## Length Class Mode
## a0 47 -none- numeric
## beta 799 dgCMatrix S4
## df 47 -none- numeric
## dim 2 -none- numeric
## lambda 47 -none- numeric
## dev.ratio 47 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 17 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.274560219 0.161777041 -0.353285032 -0.124989413 -0.093190698
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.009643051 0.078769862 -0.091108524 -0.160333965
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.27637347 0.16324101 -0.35692452 -0.13118580 -0.10036052
## YOB.Age.fctr.L YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.01732754 0.08676439 -0.09901607 -0.16937001## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Gender.fctrM 100.00000 100.00000
## YOB.Age.fctr^8 46.54960 46.54960
## Gender.fctrF 45.76022 45.76022
## Income.fctr.Q 36.15421 36.15421
## Income.fctr.C 27.35876 27.35876
## YOB.Age.fctr^7 26.88924 26.88924
## YOB.Age.fctr^6 23.43049 23.43049
## YOB.Age.fctr.L 3.92711 3.92711
## .rnorm 0.00000 0.00000
## Income.fctr.L 0.00000 0.00000
## Income.fctr^4 0.00000 0.00000
## Income.fctr^5 0.00000 0.00000
## Income.fctr^6 0.00000 0.00000
## YOB.Age.fctr.Q 0.00000 0.00000
## YOB.Age.fctr.C 0.00000 0.00000
## YOB.Age.fctr^4 0.00000 0.00000
## YOB.Age.fctr^5 0.00000 0.00000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId) 
## [1] "Min/Max Boundaries: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 6827 D 0.5592139
## 2 1531 D 0.5615027
## 3 4664 D 0.5615027
## 4 4975 D 0.5615027
## 5 6890 D 0.5615027
## 6 1486 D 0.5837958
## 7 2245 D 0.6010546
## 8 2158 D 0.6195958
## 9 3812 D 0.6195958
## 10 409 D 0.6234489
## 11 496 D 0.6294559
## 12 2101 D 0.6294559
## 13 4334 R 0.5845827
## 14 5162 R 0.5592139
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## 7 R TRUE
## 8 R TRUE
## 9 R TRUE
## 10 R TRUE
## 11 R TRUE
## 12 R TRUE
## 13 R FALSE
## 14 R FALSE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 1 0.4407861
## 2 0.4384973
## 3 0.4384973
## 4 0.4384973
## 5 0.4384973
## 6 0.4162042
## 7 0.3989454
## 8 0.3804042
## 9 0.3804042
## 10 0.3765511
## 11 0.3705441
## 12 0.3705441
## 13 0.5845827
## 14 0.5592139
## Party.fctr.All.X..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 TRUE
## 14 TRUE
## Party.fctr.All.X..rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## 13 TRUE
## 14 TRUE
## Party.fctr.All.X..rcv.glmnet.error .label
## 1 -0.14078606 6827
## 2 -0.13849733 1531
## 3 -0.13849733 4664
## 4 -0.13849733 4975
## 5 -0.13849733 6890
## 6 -0.11620424 1486
## 7 -0.09894542 2245
## 8 -0.08040421 2158
## 9 -0.08040421 3812
## 10 -0.07655115 409
## 11 -0.07054414 496
## 12 -0.07054414 2101
## 13 0.00000000 4334
## 14 0.00000000 5162
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1432 D 0.4184840
## 2 4572 D 0.4184840
## 3 775 D 0.4356302
## 4 1920 D 0.4356302
## 5 3083 D 0.4356302
## 6 3630 D 0.4356302
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.5815160 FALSE
## 2 0.5815160 FALSE
## 3 0.5643698 FALSE
## 4 0.5643698 FALSE
## 5 0.5643698 FALSE
## 6 0.5643698 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.2815160
## 2 FALSE -0.2815160
## 3 FALSE -0.2643698
## 4 FALSE -0.2643698
## 5 FALSE -0.2643698
## 6 FALSE -0.2643698
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 44 2334 D 0.4459951
## 211 5400 D 0.4830703
## 233 4043 D 0.4892190
## 304 1453 D 0.5028873
## 305 5190 D 0.5028873
## 473 4197 D 0.6073385
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 44 R TRUE
## 211 R TRUE
## 233 R TRUE
## 304 R TRUE
## 305 R TRUE
## 473 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 44 0.5540049
## 211 0.5169297
## 233 0.5107810
## 304 0.4971127
## 305 0.4971127
## 473 0.3926615
## Party.fctr.All.X..rcv.glmnet.is.acc
## 44 FALSE
## 211 FALSE
## 233 FALSE
## 304 FALSE
## 305 FALSE
## 473 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 44 FALSE
## 211 FALSE
## 233 FALSE
## 304 FALSE
## 305 FALSE
## 473 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 44 -0.25400494
## 211 -0.21692968
## 233 -0.21078103
## 304 -0.19711270
## 305 -0.19711270
## 473 -0.09266146
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 586 5920 D 0.6514068
## 587 38 D 0.6572130
## 588 1059 D 0.6572130
## 589 3439 D 0.6572130
## 590 5324 D 0.6572130
## 591 6937 D 0.6572130
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 586 R TRUE
## 587 R TRUE
## 588 R TRUE
## 589 R TRUE
## 590 R TRUE
## 591 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 586 0.3485932
## 587 0.3427870
## 588 0.3427870
## 589 0.3427870
## 590 0.3427870
## 591 0.3427870
## Party.fctr.All.X..rcv.glmnet.is.acc
## 586 FALSE
## 587 FALSE
## 588 FALSE
## 589 FALSE
## 590 FALSE
## 591 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 586 FALSE
## 587 FALSE
## 588 FALSE
## 589 FALSE
## 590 FALSE
## 591 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 586 -0.04859325
## 587 -0.04278703
## 588 -0.04278703
## 589 -0.04278703
## 590 -0.04278703
## 591 -0.04278703
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## Gender.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## M M 670 2655 837 0.5963612 0.60035842
## N N 25 88 30 0.0197664 0.02240143
## F F 421 1709 525 0.3838724 0.37724014
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## M 0.60129310 1319.88355 0.4971313 2655 336.10528
## N 0.02155172 42.84662 0.4868934 88 12.43919
## F 0.37715517 809.93291 0.4739221 1709 208.08636
## err.abs.OOB.mean
## M 0.5016497
## N 0.4975676
## F 0.4942669
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1116.000000 4452.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 2172.663089 1.457947
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4452.000000 556.630833 1.493484write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 200.691 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 18 fit.models 8 2 2 190.241 200.702 10.461
## 19 fit.models 8 3 3 200.703 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 19 fit.models 8 3 3 200.703 205.062
## 20 fit.data.training 9 0 0 205.062 NA
## elapsed
## 19 4.359
## 20 NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glbMdlSelId)) != -1))
ths_preProcess <- str_sub(glbMdlSelId, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.675000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0235 on full training set
## [1] "myfit_mdl: train complete: 3.557000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 45 -none- numeric
## beta 765 dgCMatrix S4
## df 45 -none- numeric
## dim 2 -none- numeric
## lambda 45 -none- numeric
## dev.ratio 45 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 17 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.18744022 0.16541944 -0.21406039 -0.06466942 -0.02458271
## YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.03238342 -0.02677560 -0.05570914
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Income.fctr.Q Income.fctr.C
## 0.18975694 0.16718278 -0.21886366 -0.07255626 -0.03244816
## YOB.Age.fctr^6 YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.04116529 -0.03480634 -0.06507790
## [1] "myfit_mdl: train diagnostics complete: 4.112000 secs"
## Prediction
## Reference R D
## R 2617 0
## D 2951 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700072 0.0000000 0.4568265 0.4832193 0.5299928
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "myfit_mdl: predict complete: 5.281000 secs"
## id feats
## 1 Final##rcv#glmnet YOB.Age.fctr,.rnorm,Income.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 2.869 0.085
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5563848 0.5502484 0.5625212 0.4313326
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6394624 0.5471161
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4568265 0.4832193 0.08371877
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007923569 0.01492343
## [1] "myfit_mdl: exit: 5.296000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 0 0 205.062 210.805
## 21 fit.data.training 9 1 1 210.805 NA
## elapsed
## 20 5.743
## 21 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.7glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp imp
## Gender.fctrM 100.00000 100.00000 100.00000
## Gender.fctrF 45.76022 77.15383 77.15383
## Income.fctr.Q 36.15421 30.61766 30.61766
## YOB.Age.fctr^8 46.54960 26.53818 26.53818
## YOB.Age.fctr^6 23.43049 15.63738 15.63738
## YOB.Age.fctr^7 26.88924 12.97811 12.97811
## Income.fctr.C 27.35876 11.94635 11.94635
## .rnorm 0.00000 0.00000 0.00000
## Income.fctr.L 0.00000 0.00000 0.00000
## Income.fctr^4 0.00000 0.00000 0.00000
## Income.fctr^5 0.00000 0.00000 0.00000
## Income.fctr^6 0.00000 0.00000 0.00000
## YOB.Age.fctr.C 0.00000 0.00000 0.00000
## YOB.Age.fctr.L 3.92711 0.00000 0.00000
## YOB.Age.fctr.Q 0.00000 0.00000 0.00000
## YOB.Age.fctr^4 0.00000 0.00000 0.00000
## YOB.Age.fctr^5 0.00000 0.00000 0.00000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) 
## [1] "Min/Max Boundaries: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 35 D NA
## 2 27 D NA
## 3 78 D 0.4539588
## 4 31 D 0.4612616
## 5 55 D 0.4632865
## 6 1 D 0.4749769
## 7 83 D 0.4830703
## 8 48 D NA
## 9 73 D 0.5666754
## 10 10 D NA
## 11 4 D 0.5711833
## 12 34 D 0.6029445
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 <NA> NA
## 2 <NA> NA
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## 7 R TRUE
## 8 <NA> NA
## 9 R TRUE
## 10 <NA> NA
## 11 R TRUE
## 12 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 1 NA
## 2 NA
## 3 0.5460412
## 4 0.5387384
## 5 0.5367135
## 6 0.5250231
## 7 0.5169297
## 8 NA
## 9 0.4333246
## 10 NA
## 11 0.4288167
## 12 0.3970555
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 1 NA 0.4780615
## 2 NA 0.4789935
## 3 FALSE 0.4809106
## 4 FALSE 0.4832894
## 5 FALSE 0.4856443
## 6 FALSE 0.4873064
## 7 FALSE 0.4908772
## 8 NA 0.4974626
## 9 FALSE 0.5689997
## 10 NA 0.5726229
## 11 FALSE 0.5735367
## 12 FALSE 0.5816586
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## 7 R TRUE
## 8 R TRUE
## 9 R TRUE
## 10 R TRUE
## 11 R TRUE
## 12 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 1 0.5219385
## 2 0.5210065
## 3 0.5190894
## 4 0.5167106
## 5 0.5143557
## 6 0.5126936
## 7 0.5091228
## 8 0.5025374
## 9 0.4310003
## 10 0.4273771
## 11 0.4264633
## 12 0.4183414
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSE
## 12 FALSE
## Party.fctr.Final..rcv.glmnet.error .label
## 1 -0.2219385 35
## 2 -0.2210065 27
## 3 -0.2190894 78
## 4 -0.2167106 31
## 5 -0.2143557 55
## 6 -0.2126936 1
## 7 -0.2091228 83
## 8 -0.2025374 48
## 9 -0.1310003 73
## 10 -0.1273771 10
## 11 -0.1264633 4
## 12 -0.1183414 34
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 448 D 0.418484
## 2 478 D 0.418484
## 3 1432 D NA
## 4 2349 D 0.418484
## 5 2829 D 0.418484
## 6 3557 D 0.418484
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 <NA> NA
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.581516 FALSE
## 2 0.581516 FALSE
## 3 NA NA
## 4 0.581516 FALSE
## 5 0.581516 FALSE
## 6 0.581516 FALSE
## Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1 0.4691534 R
## 2 0.4691534 R
## 3 0.4691534 R
## 4 0.4691534 R
## 5 0.4691534 R
## 6 0.4691534 R
## Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.5308466
## 2 TRUE 0.5308466
## 3 TRUE 0.5308466
## 4 TRUE 0.5308466
## 5 TRUE 0.5308466
## 6 TRUE 0.5308466
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1 FALSE -0.2308466
## 2 FALSE -0.2308466
## 3 FALSE -0.2308466
## 4 FALSE -0.2308466
## 5 FALSE -0.2308466
## 6 FALSE -0.2308466
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 55 1920 D NA
## 370 1080 D NA
## 728 2401 D 0.4795647
## 1065 4455 D 0.4892190
## 1594 2868 D 0.5331945
## 1714 2783 D 0.5643927
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 55 <NA> NA
## 370 <NA> NA
## 728 R TRUE
## 1065 R TRUE
## 1594 R TRUE
## 1714 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 55 NA
## 370 NA
## 728 0.5204353
## 1065 0.5107810
## 1594 0.4668055
## 1714 0.4356073
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 55 NA 0.4780942
## 370 NA 0.4856443
## 728 FALSE 0.4931834
## 1065 FALSE 0.4976289
## 1594 FALSE 0.5118060
## 1714 FALSE 0.5726550
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 55 R TRUE
## 370 R TRUE
## 728 R TRUE
## 1065 R TRUE
## 1594 R TRUE
## 1714 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 55 0.5219058
## 370 0.5143557
## 728 0.5068166
## 1065 0.5023711
## 1594 0.4881940
## 1714 0.4273450
## Party.fctr.Final..rcv.glmnet.is.acc
## 55 FALSE
## 370 FALSE
## 728 FALSE
## 1065 FALSE
## 1594 FALSE
## 1714 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 55 FALSE
## 370 FALSE
## 728 FALSE
## 1065 FALSE
## 1594 FALSE
## 1714 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 55 -0.2219058
## 370 -0.2143557
## 728 -0.2068166
## 1065 -0.2023711
## 1594 -0.1881940
## 1714 -0.1273450
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2946 4714 D 0.657213
## 2947 4874 D 0.657213
## 2948 5324 D NA
## 2949 6216 D 0.657213
## 2950 6503 D 0.657213
## 2951 6937 D NA
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2946 R TRUE
## 2947 R TRUE
## 2948 <NA> NA
## 2949 R TRUE
## 2950 R TRUE
## 2951 <NA> NA
## Party.fctr.All.X..rcv.glmnet.err.abs
## 2946 0.342787
## 2947 0.342787
## 2948 NA
## 2949 0.342787
## 2950 0.342787
## 2951 NA
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2946 FALSE 0.6053013
## 2947 FALSE 0.6053013
## 2948 NA 0.6053013
## 2949 FALSE 0.6053013
## 2950 FALSE 0.6053013
## 2951 NA 0.6053013
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2946 R TRUE
## 2947 R TRUE
## 2948 R TRUE
## 2949 R TRUE
## 2950 R TRUE
## 2951 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 2946 0.3946987
## 2947 0.3946987
## 2948 0.3946987
## 2949 0.3946987
## 2950 0.3946987
## 2951 0.3946987
## Party.fctr.Final..rcv.glmnet.is.acc
## 2946 FALSE
## 2947 FALSE
## 2948 FALSE
## 2949 FALSE
## 2950 FALSE
## 2951 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 2946 FALSE
## 2947 FALSE
## 2948 FALSE
## 2949 FALSE
## 2950 FALSE
## 2951 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 2946 -0.09469868
## 2947 -0.09469868
## 2948 -0.09469868
## 2949 -0.09469868
## 2950 -0.09469868
## 2951 -0.09469868
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final..rcv.glmnet.prob"
## [2] "Party.fctr.Final..rcv.glmnet"
## [3] "Party.fctr.Final..rcv.glmnet.err"
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 21 fit.data.training 9 1 1 210.805 218.399
## 22 predict.data.new 10 0 0 218.400 NA
## elapsed
## 21 7.595
## 22 NAStep 10.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] 0.7## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## Random###myrandom_classfr 0.4704301 0.5173024
## Max.cor.Y.rcv.1X1###glmnet 0.4704301 0.5075175
## Low.cor.X##rcv#glmnet 0.4704301 0.5065619
## All.X##rcv#glmnet 0.4704301 0.5065619
## All.X##rcv#glm 0.4704301 0.5050230
## Max.cor.Y##rcv#rpart 0.4704301 0.5003384
## MFO###myMFO_classfr 0.4704301 0.5000000
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## Random###myrandom_classfr 0.5263863 0.4699012
## Max.cor.Y.rcv.1X1###glmnet 0.4986850 0.4699012
## Low.cor.X##rcv#glmnet 0.4993087 0.5634945
## All.X##rcv#glmnet 0.4993087 0.5634945
## All.X##rcv#glm 0.4980276 0.5594529
## Max.cor.Y##rcv#rpart 0.4996616 0.5606472
## MFO###myMFO_classfr 0.5000000 0.4699012
## Final##rcv#glmnet NA 0.5471161
## opt.prob.threshold.fit opt.prob.threshold.OOB
## Random###myrandom_classfr 0.6 0.6
## Max.cor.Y.rcv.1X1###glmnet 0.7 0.7
## Low.cor.X##rcv#glmnet 0.7 0.7
## All.X##rcv#glmnet 0.7 0.7
## All.X##rcv#glm 0.7 0.7
## Max.cor.Y##rcv#rpart 0.7 0.7
## MFO###myMFO_classfr 0.5 0.5
## Final##rcv#glmnet 0.7 NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference R D
## R 525 0
## D 591 0## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## M 1319.88355 336.10528 1660.23197 NA
## N 42.84662 12.43919 55.78403 NA
## F 809.93291 208.08636 1027.51796 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.R .n.OOB
## M 0.5963612 0.60035842 0.60129310 2655 837 670
## N 0.0197664 0.02240143 0.02155172 88 30 25
## F 0.3838724 0.37724014 0.37715517 1709 525 421
## .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## M 1613 1712 837 2655 837 3325 0.5016497
## N 63 50 30 88 30 113 0.4975676
## F 1275 855 525 1709 525 2130 0.4942669
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## M 0.4971313 NA 0.4993179
## N 0.4868934 NA 0.4936640
## F 0.4739221 NA 0.4824028
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 2172.663089 556.630833 2743.533968 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4452.000000
## .n.New.R .n.OOB .n.Trn.D .n.Trn.R
## 1392.000000 1116.000000 2951.000000 2617.000000
## .n.Tst .n.fit .n.new .n.trn
## 1392.000000 4452.000000 1392.000000 5568.000000
## err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## 1.493484 1.457947 NA 1.475385## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Gender.fctrM 100.00000 100.00000
## YOB.Age.fctr^8 46.54960 26.53818
## Gender.fctrF 45.76022 77.15383
## Income.fctr.Q 36.15421 30.61766
## Income.fctr.C 27.35876 11.94635
## YOB.Age.fctr^7 26.88924 12.97811
## YOB.Age.fctr^6 23.43049 15.63738## [1] "glbObsNew prediction stats:"##
## R D
## 1392 0## label step_major step_minor label_minor bgn end
## 22 predict.data.new 10 0 0 218.400 229.742
## 23 display.session.info 11 0 0 229.743 NA
## elapsed
## 22 11.342
## 23 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 36.735
## 16 fit.models 8 0 0 146.606
## 17 fit.models 8 1 1 171.350
## 2 inspect.data 2 0 0 13.427
## 22 predict.data.new 10 0 0 218.400
## 18 fit.models 8 2 2 190.241
## 21 fit.data.training 9 1 1 210.805
## 1 import.data 1 0 0 6.607
## 20 fit.data.training 9 0 0 205.062
## 19 fit.models 8 3 3 200.703
## 3 scrub.data 2 1 1 31.341
## 15 select.features 7 0 0 144.592
## 11 extract.features.end 3 6 6 35.372
## 12 manage.missing.data 4 0 0 36.257
## 13 cluster.data 5 0 0 36.661
## 7 extract.features.image 3 2 2 35.165
## 10 extract.features.string 3 5 5 35.312
## 9 extract.features.text 3 4 4 35.261
## 4 transform.data 2 2 2 35.067
## 6 extract.features.datetime 3 1 1 35.128
## 8 extract.features.price 3 3 3 35.225
## 5 extract.features 3 0 0 35.108
## end elapsed duration
## 14 144.592 107.857 107.857
## 16 171.350 24.744 24.744
## 17 190.241 18.891 18.891
## 2 31.341 17.914 17.914
## 22 229.742 11.342 11.342
## 18 200.702 10.461 10.461
## 21 218.399 7.595 7.594
## 1 13.426 6.819 6.819
## 20 210.805 5.743 5.743
## 19 205.062 4.359 4.359
## 3 35.067 3.726 3.726
## 15 146.605 2.014 2.013
## 11 36.257 0.885 0.885
## 12 36.660 0.403 0.403
## 13 36.735 0.074 0.074
## 7 35.225 0.060 0.060
## 10 35.372 0.060 0.060
## 9 35.311 0.050 0.050
## 4 35.107 0.040 0.040
## 6 35.164 0.036 0.036
## 8 35.260 0.036 0.035
## 5 35.127 0.020 0.019
## [1] "Total Elapsed Time: 229.742 secs"